WSC '94 Proceedings of the 26th conference on Winter simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 2
Accounting for input model and parameter uncertainty in simulation
Proceedings of the 33nd conference on Winter simulation
Input uncertainty: accounting for parameter uncertainty in simulation input modeling
Proceedings of the 33nd conference on Winter simulation
Monte carlo computation of conditional expectation quantiles
Monte carlo computation of conditional expectation quantiles
Input modeling: input model uncertainty: why do we care and what should we do about it?
Proceedings of the 35th conference on Winter simulation: driving innovation
Input modeling: input model uncertainty: why do we care and what should we do about it?
Proceedings of the 35th conference on Winter simulation: driving innovation
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Bayesian ideas and discrete event simulation: why, what and how
Proceedings of the 38th conference on Winter simulation
Input uncertainty in outout analysis
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
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Given uncertainty in the input model and parameters of a simulation study, the goal of the simulation study often becomes the estimation of a conditional expectation. The conditional expectation is expected performance conditional on the selected model and parameters. The distribution of this conditional expectation describes precisely, and concisely, the impact of input uncertainty on performance prediction. In this paper we estimate the density of a conditional expectation using ideas from the field of kernel density estimation. We present a result on asymptotically optimal rates of convergence and examine a number of numerical examples.